Edge detection using the linear model

An edge detector based on the linear model is developed which utilizes the generalized likelihood ratio for statistical hypothesis testing. The detector is invariant to multiplicative changes in the gray-scale values of the image. Hence, thresholding based histogram segmentation is not required. The performance of this detector is analytically and experimentally compared to that of a gradient operator (Sobel) and is shown to have only a slightly poorer detection rate for a given false alarm rate.